A non-memory-based functional neural framework for animal caching behavior

Abstract

The brain’s extraordinary abilities are often attributed to its capacity to learn and adapt. But memory has its limitations, especially when faced with tasks such as retrieving thousands of food items—a common behavior in scatter-hoarding animals. Here, we propose a brain mechanism that may facilitate caching and retrieval behaviors, with a focus on hippocampal spatial cells. Rather than memorizing the locations of their caches, as previously hypothesized, we suggest that cache-hoarding animals employ a static mechanism akin to hash functions commonly used in computing. Our mathematical model aligns with the activity of hippocampal spatial cells, which respond to an animal’s positional attention. We know that the region that activates each spatial cell remains consistent across subsequent visits to the same area but not between areas. This remapping, combined with the uniqueness of cognitive maps, produces persistent hash functions that can serve both food caching and retrieval. We present a simple neural network architecture that can generate such a probabilistic hash that is unique to the animal and not sensitive to environmental changes. This mechanism could serve a virtually boundless capacity for the encoding of any structured data.